Research On The Volatility-management Strategy Based On Factor

The concept of volatility is very important in finance.Since Markowitz proposed the mean-variance model,volatility has always been an important indicator to measure risk and has attracted the attention of academics and investors.According to the negative correlation between the lagged volatility and current return/volatility in the US stock market,Moreira and Muir(2017)[25]proposed a "Volatility-managed Portfolios":When the volatility is relatively low,people should increase the risk exposure weight of factors;vice versa.This strategy has performed well in the US stock market.This article studies the effectiveness of this research in the A-share market in the context of single-factor and multi-factor.Firstly,based on the research results of Moreira and Muir(2017)[25]and the development of mainstream multi-factor models,this paper selects 12 representative factors as the research objects:market factor(Mkt),scale factor(SMB),value factor(HML),profit factor(RMW),investment factor(CMA)in the Fama-French five-factor model,investment factor(IA)and profit factor(ROE)in the q-factor model,momentum factor(Mom)of the Carhart four-factor model,management factor(MGMT),performance factor(PERF)in the Stambaugh-Yuan four-factor model,and the long-horizon behavior factor(FIN)and short-horizon behavioral factor(PEAD)in the Daniel-Hirshleifer-Yuan three-factor model.In the context of the A-share market,the article explored whether the factors can generate significant alpha and increase the Sharpe Ratio after volatility management.From the regression results of the volatility management portfolio and the original factor rate,we can see that the volatility-management strategy does not improve the single factor rate of return very well,and only the alpha corresponding to the Mkt and PERF factors is significantly positive.In addition,the Sharpe Ratio of SMB,Mom and PEAD factors has also been improved.In order to reduce the impact of the small sample size of the A-share market on the regression results,this article increases the frequency of the "Volatility-managed Portfolios" from monthly to weekly,but the performance has not been significantly improved.In addition,considering that investors' preferences for upside risks and downside risks are asymmetrical,this paper introduces the "Downside Volatility-managed Portfolios" proposed by Qiao X et al.(2020)[27].Compared with the performance of volatility-managed portfolios and with the exception of the PERF factor,the downside volatility-management portfolio which corresponded to the alpha is larger.Secondly,this paper tests the effectiveness of volatility-management strategy in a multi-factor environment by constructing multi-factor portfolios.Based on the source of asset income represented by factors and correlation analysis,this article selects six application scenarios:Fama-French three-factor(FF3),Fama-French three-factor and momentum factor(FF3&Mom),Fama-French five-factor(FF5),Fama-French five factors and momentum factors(FF5&Mom),behavioral finance factor(MGM&PERF&FIN&PEAD)and all factors(all).The strategy of monthly frequency reallocation is significantly effective only in the case of FF3,and the strategy of weekly frequency reallocation is significantly effective for FF3 and FF3&Mom.However,in the field of multi-factor allocation,whether Sharpe Ratio or the cumulative return,volatility-managed portfolios and downside volatility-managed portfolios perform better than common factor allocation strategies such as equal weight strategies and risk parity strategies.It can be seen that in the A-share market,volatility-managed portfolios have certain guiding significance for multi-factor allocation.Finally,take the China Securities Index as the research object,this paper analyzes the relationship between lagged volatility,current volatility,average returns.Combining with the performance of volatility-managed portfolios in the Chinese capital market,this paper draws a conclusion:The negative correlation between lagged volatility and current return/volatility seems to have a major impact on the success of volatility management.In addition,after eliminating the look-ahead bias of the model,the performance of Volatility-managed Portfolios has not changed widely.Through the Bai&Perron structural breakpoint test,it is found that the unary linear regression model of the volatility management portfolio and the original factor excess return has all breakpoints at the 5%significance level.At this time,it is inaccurate that the full sample regression is used to obtain the alpha.That is,the alpha of the strategy may come from the wrong setting of the model.